Journal ArticleDOI
An Adaptive Distributionally Robust Model for Three-Phase Distribution Network Reconfiguration
TLDR
A distributionally robust model for three-phase unbalanced DNR is proposed to obtain the optimal configuration under the worst-case PD of DG outputs and loads within the ambiguity set and inherits the advantages of stochastic optimization and robust optimization.Abstract:
Distributed generator (DG) volatility has a great impact on system operation, which should be considered beforehand due to the slow time scale of distribution network reconfiguration (DNR). However, it is difficult to derive accurate probability distributions (PDs) for DG outputs and loads analytically. To remove the assumptions on accurate PD knowledge, a deep neural network is first devised to learn the reference joint PD from historical data in an adaptive way. The reference PD along with the forecast errors are enveloped by a distributional ambiguity set using Kullback-Leibler divergence. Then a distributionally robust model for three-phase unbalanced DNR is proposed to obtain the optimal configuration under the worst-case PD of DG outputs and loads within the ambiguity set. The result inherits the advantages of stochastic optimization and robust optimization. Finally, a modified column-and-constraint generation method with efficient scenario decomposition is investigated to solve this model. Numerical tests are carried out using an IEEE unbalanced benchmark and a practical-scale system in Shandong, China. Comparison with the deterministic, stochastic and robust DNR methods validates the effectiveness of the proposed method.read more
Citations
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Journal ArticleDOI
Two-Stage Volt/Var Control in Active Distribution Networks With Multi-Agent Deep Reinforcement Learning Method
Xianzhuo Sun,Jing Qiu +1 more
TL;DR: A two-stage deep reinforcement learning (DRL)-based real-time VVC method to mitigate fast voltage violation while minimizing the network power loss is proposed.
Journal ArticleDOI
Review and prospect of data-driven techniques for load forecasting in integrated energy systems
TL;DR: In this article , a comprehensive overview of the data-driven approaches for load forecasting in integrated energy systems (IESs) can be found, where federated learning is a promising solution for coordinated load forecasting among diverse energy sectors.
Journal ArticleDOI
Real-time autonomous dynamic reconfiguration based on deep learning algorithm for distribution network
TL;DR: A novel real-time autonomous dynamic reconfiguration (ADR) method to reduce the cost of power loss and switch action of distribution network based on the deep learning (DL) algorithm that can be decision-making from the historical control dataset and the real- time system state.
Journal ArticleDOI
A Non-Iterative Decoupled Solution for Robust Integrated Electricity-Heat Scheduling Based on Network Reduction
TL;DR: In this article, a two-stage robust model considering electricity and heat uncertainties is formulated, and the inapplicability of conventional algorithms in decoupling this model is analyzed in depth.
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